pkg/R/generateXY.R

   1 #' generateXY
   2 #'
   3 #' Generate a sample of (X,Y) of size n
   4 #'
   5 #' @param n sample size
   6 #' @param prop proportion for each cluster
   7 #' @param meanX matrix of group means for covariates (of size p)
   8 #' @param covX covariance for covariates (of size p*p)
   9 #' @param beta regression matrix, of size p*m*k
  10 #' @param covY covariance for the response vector (of size m*m)
  11 #'
  12 #' @return list with X (of size n*p) and Y (of size n*m)
  13 #'
  14 #' @export
  15 generateXY <- function(n, prop, meanX, beta, covX, covY)
  16 {
  17   p <- dim(covX)[1]
  18   m <- dim(covY)[1]
  19   k <- dim(beta)[3]
  20
  21   X <- matrix(nrow = 0, ncol = p)
  22   Y <- matrix(nrow = 0, ncol = m)
  23
  24   # random generation of the size of each population in X~Y (unordered)
  25   sizePop <- stats::rmultinom(1, n, prop)
  26   class <- c() #map i in 1:n --> index of class in 1:k
  27
  28   for (i in 1:k)
  29   {
  30     class <- c(class, rep(i, sizePop[i]))
  31     newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
  32     X <- rbind(X, newBlockX)
  33     Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*%
  34       beta[, , i], covY[,]))))
  35   }
  36
  37   shuffle <- sample(n)
  38   list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
  39 }